OpenRaiser/Intern-Atlas huggingface dataset:
Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition
Venue: arXiv cs.CL 2018
Year: 2018

Natural language definitions of terms can serve as a rich source of knowledge, but structuring them into a comprehensible semantic model is essential to enable them to be used in semantic interpretation tasks. We propose a method and provide a set of tools for automatically building a graph world knowledge base from natural language definitions. Adopting a conceptual model composed of a set of semantic roles for dictionary definitions, we trained a classifier for automatically labeling definitions, preparing the data to be later converted to a graph representation. WordNetGraph, a knowledge graph built out of noun and verb WordNet definitions according to this methodology, was successfully used in an interpretable text entailment recognition approach which uses paths in this graph to provide clear justifications for entailment decisions.
Histological images segmentation of mucous glands
Venue: arXiv cs.CV 2018
Year: 2018

Mucous glands lesions analysis and assessing of malignant potential of colon polyps are very important tasks of surgical pathology. However, differential diagnosis of colon polyps often seems impossible by classical methods and it is necessary to involve computer methods capable of assessing minimal differences to extend the capabilities of the classical pathology examination. Accurate segmentation of mucous glands from histology images is a crucial step to obtain reliable morphometric criteria for quantitative diagnostic methods. We review major trends in histological images segmentation and design a new convolutional neural network for mucous gland segmentation.
Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
Venue: arXiv cs.CV 2018
Year: 2018

Registration of pre-operative 3-D volumes to intra-operative 2-D X-ray images is important in minimally invasive medical procedures. Rigid registration can be performed by estimating a global rigid motion that optimizes the alignment of local correspondences. However, inaccurate correspondences challenge the registration performance. To minimize their influence, we estimate optimal weights for correspondences using PointNet. We train the network directly with the criterion to minimize the registration error. We propose an objective function which includes point-to-plane correspondence-based motion estimation and projection error computation, thereby enabling the learning of a weighting strategy that optimally fits the underlying formulation of the registration task in an end-to-end fashion. For single-vertebra registration, we achieve an accuracy of 0.74$\pm$0.26 mm and highly improved robustness. The success rate is increased from 79.3 % to 94.3 % and the capture range from 3 mm to 13 mm.
Disentangling Multiple Conditional Inputs in GANs
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs). In particular, we demonstrate our method in controlling color, texture, and shape of a generated garment image for computer-aided fashion design. To disentangle the effect of input attributes, we customize conditional GANs with consistency loss functions. In our experiments, we tune one input at a time and show that we can guide our network to generate novel and realistic images of clothing articles. In addition, we present a fashion design process that estimates the input attributes of an existing garment and modifies them using our generator.
A Narrative Literature Review and E-Commerce Website Research
Venue: arXiv cs.CY 2018
Year: 2018

In this study, a narrative literature review regarding culture and e-commerce website design has been introduced. Cultural aspect and e-commerce website design will play a significant role for successful global e-commerce sites in the future. Future success of businesses will rely on e-commerce. To compete in the global e-commerce marketplace, local businesses need to focus on designing culturally friendly e-commerce websites. To the best of my knowledge, there has been insignificant research conducted on correlations between culture and e-commerce website design. The research shows that there are correlations between e-commerce, culture, and website design. The result of the study indicates that cultural aspects influence e-commerce website design. This study aims to deliver a reference source for information systems and information technology researchers interested in culture and e-commerce website design, and will show lessfocused research areas in addition to future directions.
A Look at Motion Planning for Autonomous Vehicles at an Intersection
Venue: arXiv cs.RO 2018
Year: 2018

Autonomous Vehicles are currently being tested in a variety of scenarios. As we move towards Autonomous Vehicles, how should intersections look? To answer that question, we break down an intersection management into the different conundrums and scenarios involved in the trajectory planning and current approaches to solve them. Then, a brief analysis of current works in autonomous intersection is conducted. With a critical eye, we try to delve into the discrepancies of existing solutions while presenting some critical and important factors that have been addressed. Furthermore, open issues that have to be addressed are also emphasized. We also try to answer the question of how to benchmark intersection management algorithms by providing some factors that impact autonomous navigation at intersection.
Gamification for Education of the Digitally Native Generation by Means of Virtual Reality, Augmented Reality, Machine Learning, and Brain-Computing Interfaces in Museums
Venue: arXiv cs.CY 2018
Year: 2018

Particularly close attention is being paid today among researchers in social science disciplines to aspects of learning in the digital age, especially for the Digitally Native Generation. In the context of museums, the question is: how can rich learning experiences be provided for increasingly technologically advanced young visitors in museums? Which high-tech platforms and solutions do museums need to focus on? At the same time, the software games business is growing fast and now finding its way into non-entertainment contexts, helping to deliver substantial benefits, particularly in education, training, research, and health. This article outlines some aspects facing Digitally Native learners in museums through an analysis of several radically new key technologies: Interactivity, Wearables, Virtual Reality, and Augmented Reality. Special attention is paid to use cases for application of games-based scenarios via these technologies in non-leisure contexts and specifically for educational purposes in museums.
Multi-Layer Ensembling Techniques for Multilingual Intent Classification
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we determine how multi-layer ensembling improves performance on multilingual intent classification. We develop a novel multi-layer ensembling approach that ensembles both different model initializations and different model architectures. We also introduce a new banking domain dataset and compare results against the standard ATIS dataset and the Chinese SMP2017 dataset to determine ensembling performance in multilingual and multi-domain contexts. We run ensemble experiments across all three datasets, and conclude that ensembling provides significant performance increases, and that multi-layer ensembling is a no-risk way to improve performance on intent classification. We also find that a diverse ensemble of simple models can reach perform comparable to much more sophisticated state-of-the-art models. Our best F 1 scores on ATIS, Banking, and SMP are 97.54%, 91.79%, and 93.55% respectively, which compare well with the state-of-the-art on ATIS and best submission to the SMP2017 competition. The total ensembling performance increases we achieve are 0.23%, 1.96%, and 4.04% F 1 respectively.
Gradient Adversarial Training of Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a prior belief that in many contexts, simultaneous gradient updates should be statistically indistinguishable from each other. We enforce this consistency using an auxiliary network that classifies the origin of the gradient tensor, and the main network serves as an adversary to the auxiliary network in addition to performing standard task-based training. We demonstrate gradient adversarial training for three different scenarios: (1) as a defense to adversarial examples we classify gradient tensors and tune them to be agnostic to the class of their corresponding example, (2) for knowledge distillation, we do binary classification of gradient tensors derived from the student or teacher network and tune the student gradient tensor to mimic the teacher's gradient tensor; and (3) for multi-task learning we classify the gradient tensors derived from different task loss functions and tune them to be statistically indistinguishable. For each of the three scenarios we show the potential of gradient adversarial training procedure. Specifically, gradient adversarial training increases the robustness of a network to adversarial attacks, is able to better distill the knowledge from a teacher network to a student network compared to soft targets, and boosts multi-task learning by aligning the gradient tensors derived from the task specific loss functions. Overall, our experiments demonstrate that gradient tensors contain latent information about whatever tasks are being trained, and can support diverse machine learning problems when intelligently guided through adversarialization using a auxiliary network.
Towards a Grounded Dialog Model for Explainable Artificial Intelligence
Venue: arXiv cs.AI 2018
Year: 2018

To generate trust with their users, Explainable Artificial Intelligence (XAI) systems need to include an explanation model that can communicate the internal decisions, behaviours and actions to the interacting humans. Successful explanation involves both cognitive and social processes. In this paper we focus on the challenge of meaningful interaction between an explainer and an explainee and investigate the structural aspects of an explanation in order to propose a human explanation dialog model. We follow a bottom-up approach to derive the model by analysing transcripts of 398 different explanation dialog types. We use grounded theory to code and identify key components of which an explanation dialog consists. We carry out further analysis to identify the relationships between components and sequences and cycles that occur in a dialog. We present a generalized state model obtained by the analysis and compare it with an existing conceptual dialog model of explanation.
Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification
Venue: arXiv cs.CV 2018
Year: 2018

Recognition of surgical gesture is crucial for surgical skill assessment and efficient surgery training. Prior works on this task are based on either variant graphical models such as HMMs and CRFs, or deep learning models such as Recurrent Neural Networks and Temporal Convolutional Networks. Most of the current approaches usually suffer from over-segmentation and therefore low segment-level edit scores. In contrast, we present an essentially different methodology by modeling the task as a sequential decision-making process. An intelligent agent is trained using reinforcement learning with hierarchical features from a deep model. Temporal consistency is integrated into our action design and reward mechanism to reduce over-segmentation errors. Experiments on JIGSAWS dataset demonstrate that the proposed method performs better than state-of-the-art methods in terms of the edit score and on par in frame-wise accuracy. Our code will be released later.
Lamarckian Evolution of Convolutional Neural Networks
Venue: arXiv cs.NE 2018
Year: 2018

Convolutional neural networks belong to the most successul image classifiers, but the adaptation of their network architecture to a particular problem is computationally expensive. We show that an evolutionary algorithm saves training time during the network architecture optimization, if learned network weights are inherited over generations by Lamarckian evolution. Experiments on typical image datasets show similar or significantly better test accuracies and improved convergence speeds compared to two different baselines without weight inheritance. On CIFAR-10 and CIFAR-100 a 75 % improvement in data efficiency is observed.
Strict Linearizability and Abstract Atomicity
Venue: arXiv cs.SE 2018
Year: 2018

Linearizability is a commonly accepted consistency condition for concurrent objects. Filipović et al. show that linearizability is equivalent to observational refinement. However, linearizability does not permit concurrent objects to share memory spaces with their client programs. We show that linearizability (or observational refinement) can be broken even though a client program of an object accesses the shared memory spaces without interference from the methods of the object. In this paper, we present strict linearizability which lifts this limitation and can ensure client-side traces and final-states equivalence even in a relaxed program model allowing clients to directly access the states of concurrent objects. We also investigate several important properties of strict linearizability. At a high level of abstraction, a concurrent object can be viewed as a concurrent implementation of an abstract data type (ADT). We also present a correctness criterion for relating an ADT and its concurrent implementation, which is the combination of linearizability and data abstraction and can ensure observational equivalence. We also investigate its relationship with strict linearizability.
Behavior-based evaluation of session satisfaction
Venue: arXiv cs.IR 2018
Year: 2018

Nowadays, web search becomes more and more popular all over the world. Many researchers and developers have done lots of studies on behaviors of search users. In practice, the full understanding of these behaviors can not only help to evaluate the usefulness of newly-developed ranking algorithms and other changes of search engine, but also to guide the growth direction of search engine. As far as we know, most of past work are mainly focused on single search evaluation, which do promote the rapid development of search engine in early stage. However,these page-level behaviors are so limited that can no longer give explicit feedbacks on minor changes of the search engine. We think that it will be more accurate and sensitive when more information on search session are provided. In this paper, a session level evaluation method is proposed. The session-level features are retrieved and carefully analyzed. Some linear and non-linear features which can reflect the final degree of satisfaction are chosen and adopted in evaluation models. A two-layer hybrid evaluation model with different granularity, which can achieve good precision and recall, is designed and trained. Lots of real experiments are evaluated by the model, the result shows it achieved a higher accuracy performance than traditional page-level evaluation metrics. Furthermore, for practical application, it is important to interpret the reason of each session's satisfaction judgement. In all, a session-level evaluation model with improved performance and well capability on interpretation is proposed and applied in real practice in search engine companies.
CaloriNet: From silhouettes to calorie estimation in private environments
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.
Crowd disagreement about medical images is informative
Venue: arXiv cs.CV 2018
Year: 2018

Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds. However, disagreement between annotators may be informative, and thus removing it may not be the best strategy. As a proof of concept, we predict whether a skin lesion from the ISIC 2017 dataset is a melanoma or not, based on crowd annotations of visual characteristics of that lesion. We compare using the mean annotations, illustrating consensus, to standard deviations and other distribution moments, illustrating disagreement. We show that the mean annotations perform best, but that the disagreement measures are still informative. We also make the crowd annotations used in this paper available at \url{https://figshare.com/s/5cbbce14647b66286544}.
Instance-Optimality in the Noisy Value-and Comparison-Model --- Accept, Accept, Strong Accept: Which Papers get in?
Venue: arXiv cs.DS 2018
Year: 2018

Motivated by crowdsourced computation, peer-grading, and recommendation systems, Braverman, Mao and Weinberg [STOC'16] studied the \emph{query} and \emph{round} complexity of fundamental problems such as finding the maximum (\textsc{max}), finding all elements above a certain value (\textsc{threshold-$v$}) or computing the top$-k$ elements (\textsc{Top}-$k$) in a noisy environment. For example, consider the task of selecting papers for a conference. This task is challenging due the crowdsourcing nature of peer reviews: the results of reviews are noisy and it is necessary to parallelize the review process as much as possible. We study the noisy value model and the noisy comparison model: In the \emph{noisy value model}, a reviewer is asked to evaluate a single element: "What is the value of paper $i$?" (\eg accept). In the \emph{noisy comparison model} (introduced in the seminal work of Feige, Peleg, Raghavan and Upfal [SICOMP'94]) a reviewer is asked to do a pairwise comparison: "Is paper $i$ better than paper $j$?" In this paper, we show optimal worst-case query complexity for the \textsc{max},\textsc{threshold-$v$} and \textsc{Top}-$k$ problems. For \textsc{max} and \textsc{Top}-$k$, we obtain optimal worst-case upper and lower bounds on the round vs query complexity in both models. For \textsc{threshold}-$v$, we obtain optimal query complexity and nearly-optimal round complexity, where $k$ is the size of the output) for both models. We then go beyond the worst-case and address the question of the importance of knowledge of the instance by providing, for a large range of parameters, instance-optimal algorithms with respect to the query complexity. Furthermore, we show that the value model is strictly easier than the comparison model.
InfoCatVAE: Representation Learning with Categorical Variational Autoencoders
Venue: arXiv cs.LG 2018
Year: 2018

This paper describes InfoCatVAE, an extension of the variational autoencoder that enables unsupervised disentangled representation learning. InfoCatVAE uses multimodal distributions for the prior and the inference network and then maximizes the evidence lower bound objective (ELBO). We connect the new ELBO derived for our model with a natural soft clustering objective which explains the robustness of our approach. We then adapt the InfoGANs method to our setting in order to maximize the mutual information between the categorical code and the generated inputs and obtain an improved model.
Clipping noise approximate analysis and power allocation for photon-detection-based DCO-OFDM and ACO-OFDM
Venue: arXiv cs.IT 2018
Year: 2018

The clipping noise of the photon-level detector for both direct current-biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) is investigated. Based on Bussgang theorem and central limit theorem (CLT), we obtain the approximate closed-form SNR of each subcarrier, based on which we further formulate the power allocation among the subcarriers. Numerical results show that the SNR obtained from theoretical analysis can well approximate that obtained from simulation results, and uniform power allocation suffices to perform close to the optimized power allocation from Genetic Algorithm (GA) with significantly reduced computational complexity.
Towards Commodity, Web-Based Augmented Reality Applications for Research and Education in Chemistry and Structural Biology
Venue: arXiv cs.HC 2018
Year: 2018

This article reports prototype web apps that use commodity, open-source technologies for augmented and virtual reality to provide immersive, interactive human-computer interfaces for chemistry, structural biology and related disciplines. The examples, which run in any standard web browser and are accessible at https://lucianoabriata.altervista.org/jsinscience/arjs/armodeling/ together with demo videos, showcase applications that could go well beyond pedagogy, i.e. advancing actual utility in research settings: molecular visualization at atomistic and coarse-grained levels in interactive immersive 3D, coarse-grained modeling of molecular physics and chemistry, and on-the-fly calculation of experimental observables and overlay onto experimental data. From this playground, I depict perspectives on how these emerging technologies might couple in the future to neural network-based quantum mechanical calculations, advanced forms of human-computer interaction such as speech-based communication, and sockets for concurrent collaboration through the internet -all technologies that are today maturing in web browsers- to deliver the next generation of tools for truly interactive, immersive molecular modeling that can streamline human thought and intent with the numerical processing power of computers.
Interpretable Discovery in Large Image Data Sets
Venue: arXiv cs.LG 2018
Year: 2018

Automated detection of new, interesting, unusual, or anomalous images within large data sets has great value for applications from surveillance (e.g., airport security) to science (observations that don't fit a given theory can lead to new discoveries). Many image data analysis systems are turning to convolutional neural networks (CNNs) to represent image content due to their success in achieving high classification accuracy rates. However, CNN representations are notoriously difficult for humans to interpret. We describe a new strategy that combines novelty detection with CNN image features to achieve rapid discovery with interpretable explanations of novel image content. We applied this technique to familiar images from ImageNet as well as to a scientific image collection from planetary science.